Tribomechanical Comparison between PVA Hydrogels Obtained Using Different Processing Conditions and Human Cartilage

Designing materials for cartilage replacement raises several challenges due to the complexity of the natural tissue and its unique tribomechanical properties. Poly(vinyl alcohol) (PVA) hydrogels have been explored for such purpose since they are biocompatible, present high chemical stability, and their properties may be tailored through different strategies. In this work, the influence of preparation conditions of PVA hydrogels on its morphology, water absorption capacity, thermotropic behavior, mechanical properties, and tribological performance was evaluated and compared with those of human cartilage (HC). The hydrogels were obtained by cast-drying (CD) and freeze-thawing (FT), in various conditions. It was found that the method of preparation of the PVA hydrogels critically affects their microstructure and performance. CD gels presented a denser structure, absorbed less water, were stiffer, dissipated less energy, and withstood higher loads than FT gels. Moreover, they led to friction coefficients against stainless steel comparable with those of HC. Overall, CD hydrogels had a closer performance to natural HC, when compared to FT ones.info:eu-repo/semantics/publishedVersio

Rare Primary Dyslipidaemias Associated with Low LDL and HDL Cholesterol Values in Portugal

Background: Dyslipidaemia represents a group of disorders of lipid metabolism, characterized by either an increase or decrease in lipid particles, usually associated with triglycerides, LDL cholesterol (LDL-C) and/or HDL cholesterol (HDL-C). Most hyperlipidaemias and HDL deficiencies confer an increased cardiovascular risk, while hypolipidaemia, such as abeta or hypobetalipoproteinemia, may present different manifestations ranging from poor weight progression to neurological manifestations. The aim of this study is to present 7 cases with rare dyslipidaemias associated with low LDL or low HDL cholesterol values, referred to our laboratory for the genetic identification of the cause of the dyslipidaemia. Methods: Lipid profile was determined for each individual in an automated equipment Integra Cobas (Roche). Molecular analysis was performed by NGS with a target panel of 57 genes involved in lipid metabolism (Sure select QXT, Agilent) and samples were run in a NextSEQ Sequencer (Illumina). Only genes associated to rare forms of low HDL-c or LDL-c were analysed for this work, namely: ABCA1, APOA1, LCAT, SCARB1, APOB, PCSK9, MTTP, SAR1B, and ANGPTL3. All rare variants (MAFA) and the other is a possible compound heterozygous for APOB variants c.2604+1G>A and c.4651C>T/p.(Gln1551*). In two patients only a variant in heterozygosity (c.3365delG/p.(Gly1122Vfs*62) and c.11095A>T/p.(Arg3699*)). In the remaining patient, no variants were identified. NGS proved to be a fundamental key for genetic testing of rare lipid disorders, allowing us to find the genetic cause of disease in 6/7 patients with low HDL-c and LDL-c. Patients with these rare conditions should be identified as early as possible in order to minimize or prevent clinical manifestations. The unsolved case is still under investigation.info:eu-repo/semantics/publishedVersio

CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images

Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase-polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method; however, its accuracy in detection is only ~70�75. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80�98, but similar accuracy of 70. To enhance the accuracy of CT imaging detection, we developed an open-source framework, CovidCTNet, composed of a set of deep learning algorithms that accurately differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 95 compared to radiologists (70). CovidCTNet is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. To facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and model parameter details as open-source. Open-source sharing of CovidCTNet enables developers to rapidly improve and optimize services while preserving user privacy and data ownership. © 2021, The Author(s)